Barca Reflections + How to Choose Your Energy

At Castello de S. Jorge in Lisbon, Portugal.

I will admit, I have not been as good about journaling and blogging as I wanted to be in Spain... I can't believe it has been a month since my last post. I thought I would post a recap every week, but I have really been trying to focus on being present in the moment and not worrying about documenting every single thing on camera which has been really refreshing. I've mentioned my thought provoking morning walks a zillion times, but it has been my time that I absolutely cherish in the morning, by myself just listening to a podcast. I reflect every single day and I need to be better about getting those reflections in writing, so that's what you are reading today.

Both photos from Lisbon, aka my new favorite place on Earth.

Before I came over to Barcelona, I was really stressed on how I would be able to create a peaceful routine that was similar to what I had been trying to do at home. I'm a morning person so it's really important for me to set myself up for success in my morning routine. Thankfully, Headspace has changed the game for me and I am still able to meditate almost every day. During the week, I wake up a little earlier and sit in my bed in meditate and if I'm traveling, I can do it on the bus to the airport or on a plane. It changes my mood drastically and gives me space in my mind to focus on what really matters throughout my day. I highly recommend it.

Today in my meditation the idea was presented that anxiety and negative feelings are like dark clouds covering up a blue sky. The blue sky is always there, but sometimes the clouds are in the way. Similarly, I guess we can think of our dreams and the person we're constantly striving to be as our own personal blue sky. Some days are cloudy, but other days we feel on top of the world, sunny and 75. I guess the take home message here (for me anyways) is that the person that we want to be is who we already are, the clouds get in the way- our fears, self imposed limitations, etc. The difference between the weather and our lives is that every day we are faced with a choice: is it going to be a sunny day or a stormy day? We get to choose. A lot of times, my anxiety or negative attitude follows me because it's a habit that I can't kick. I'm used to feeling anxious and, although I'm uncomfortable, it's an easier and more comfortable choice. However, what we choose to feel every day is either revealing or covering up that blue sky.

Beautiful blue sky views from Castello de S. Jorge.

Time Out Market Lisboa

For me, my focus right now is finding peace. Being at peace in my mind, spirit, and body, but also with the negative energy that has come into my life in the past. Choosing a positive energy is quite simple if we allow it to be. Choosing to say no to negative people, situations, or energies is the way to move forward and get to a peaceful state. I know you have experienced some form of negative energy at some point in your life. You can feel it the second you walk in the room! But have you ever thought to yourself, "you know what? I'm actually going to say no to letting this into my life." It's something I recently realized on a morning walk and am incorporating that into my journey to finding peace. It seems crazy and maybe even a little silly to place such an emphasis on just saying no, but it's actually harder than you might think.

I posted about this on my Instagram story last week, but I was working on a school project with two girls that I had just met and their energy was so positive, it was contagious. It really lit me up and made me feel like I needed to share that energy and that kindness and joy to everyone. So often, we dump our problems on other people- it's normal and it's okay to a certain extent. But we all have that one friend or even those couple of days where we are the friend that is "the dumper": filling up someone else's time and energy with whatever negativity and stress is going on in our life. Unfortunately, I can think of a couple times where I've been a dumper.

Kind of similarly, something I've noticed since being away is that I let the thoughts, opinions, and actions of other people have too significant of an impact on my energy. Whether it's thoughts on my future career or my relationship or not getting some validation or credit that I think I've earned, it seems like things I can't really change are negatively impacting my energy. What's even more frustrating is that I'm not saying no! I'm the one that is allowing that into my life.

Snaps from my daily walk in Barcelona.

My roomie, fellow Gemini, and soul sister Jess. Seriously could not be more thankful that the universe brought us together.

Circling back to my main point here, after I met with those girls, I felt so inspired and energetic and really motivated to only be a positive energy for someone else. It can change someone's whole day to share your light and kindness instead of being a negativity dumper and I realized in that moment that that is exactly who I want to be. We have the power literally every second of every day to choose the blue sky. When we walk into room, we are choosing to either share a positive energy or a negative one. We all make mistakes, but being able to be aware of the present moment and check yourself is what counts.

Wow that was a lot! If you're still reading, you rock! I guess I've had a lot on my mind in the past month haha. While I share a lot about my life on my blog, I always try to think about what I want the takeaway to be and, lately, I think my focus is being able to share some positive energy. In a world where there seems to be so much negativity, my goal is to just share my journey to choosing to find the light and good energy in every moment.

Thanks for reading. xx

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Why girls individuals in most cases have a tendency to have dodgy hip implants or other scientific devices – The Dialog AU

The past one year has seen large pain about the safety of scientific implants. About a of the worst scandals have eager devices for girls individuals, similar to textured breast implants with links to most cancers, and transvaginal mesh implants, which had been the matter of a senate inquiry.

Nonetheless girls individuals are harmed no longer easiest by “girls individuals’s devices” similar to breast implants and vaginal mesh. Females are furthermore extra liable to be harmed by it sounds as if gender-honest devices, adore joint replacements and heart implants.

Read extra:
We can prick non-public successfully being insurance charges by fixing how we pay for hip replacements and other implants

In no longer too long ago published analysis, I explored the causes for this. I came upon gender biases in any admire stages of invent and use of scientific implants.

Proposed changes to how devices are regulated, similar to introducing a national register of all implants, will fabricate it sooner to identify dodgy devices. Nonetheless this is no longer going to contend with gender bias in how devices are designed and venerable.

Bias begins with invent, then lab testing

Biological and social factors can have an affect on how girls individuals fresh when injured or sick, and how successfully treatments work. Customarily, gadget designers stop no longer buy these differences into story.

The lab assessments venerable to manufacture distinct that implants are true in most cases ignore the possibility girls individuals might maybe maybe have a sort of reactions to supplies, or their actions might maybe maybe maybe order a sort of loads on implants.

Read extra:
Treatment’s gender revolution: how girls individuals stopped being handled as ‘tiny males’

Bias continues with scientific trials

Some scientific gadget companies have exploited regulatory loopholes to discover devices to market in the UK without scientific trials. Even when trials buy order, girls individuals must no longer continuously incorporated. Or researchers stop no longer analyse the records for gender differences.

Regulators such because the Meals and Drug Administration (FDA) in the USA and Australia’s Therapeutic Goods Administration (TGA) in most cases approve devices without data split by gender on how successfully devices produce or how true they’re.

Then there’s the doctor-affected person relationship

The gender of the doctor and affected person can fabricate a distinction to what girls individuals obtain out about their implant. The very low numbers of girls individuals in surgery imply female sufferers in most cases look male surgeons. And there might be some proof male doctors have a tendency to treat female sufferers in a extra paternalistic and less affected person-centred technique.

Then there’s the matter of whether or no longer surgeons raise crucial security factors with their female sufferers. For instance, some surgeons feel unhappy discussing whether or no longer it’s true to have intercourse after a hip implant, especially with female sufferers. This is fundamental as some sexual positions are safer than others.

Read extra:
Females have heart attacks too, but their signs are regularly pushed apart as something else

Let’s stare upon one example, a hip implant

The DePuy ASR (articular surface replacement) hip implant precipitated extreme issues for sufferers spherical the realm, including Australia, similar to irritation, painful growths, dislocations and steel toxicity.

Despite media protection, few were conscious this hip implant was greater than twice as likely to fail in girls individuals.

Within the case of hip implants, the identical units are readily in the market for girls individuals and males, implying these devices are gender honest. Most units come in a fluctuate of sizes, with some having greater outcomes for girls individuals. Nonetheless girls individuals must no longer tiny males, and there are gender differences in frequent actions exciting the hip, similar to walking.

When girls individuals arise from sitting or have intercourse, the still edges of their hip sockets have a tendency to undergo greater loads than males’s. This “edge loading” increases the probability hip implants will set apart on down and unencumber dangerous steel particles.

When girls individuals arise from sitting, their hip sockets have a tendency to undergo greater loads than males’s.
from www.shutterstock.com

These differences would no longer matter if lab assessments confirmed equal security of hip implants for girls and males individuals. Then again, proof is no longer aloof on this. In level of reality, worldwide requirements for lab assessments that measure charges of set apart on and hunch in hip implants ignore gender differences, and easiest take a look at commonplace walking rather than extra annoying actions, similar to operating or having intercourse.

The FDA, TGA and other regulators in most cases approve fresh hip implants primarily primarily based entirely on their similarity to already licensed units. This took order with the ASR hip implant. Regulators did no longer need fresh data, let on my own proof the hip was equally true for girls individuals and males.

It’s a pain internationally

Internationally, there might be increasing pain about the laws and security of scientific implants. A world community of journalists released a damning story late closing one year. The FDA’s 510(k) project, which approves fresh devices primarily primarily based entirely on their similarity to present ones, allows approval of some high-possibility implants without additional proof.

The scenario isn’t any greater in Europe, the set apart business companies stop the approvals. This draw has been criticised for approving devices without ravishing data and for lacking transparency in phrases of implant recalls.

Read extra:
What’s the Clinical Technology Association and how does it wield its energy?

The FDA has taken some steps to contend with gender bias, issuing steering for companies to offer data on their devices in girls individuals and males. Then again, here’s no longer binding. A understand of devices licensed after its introduction came upon easiest 17% incorporated data analysis by intercourse.

How might maybe maybe maybe we give a capture to issues?

In Australia, scandals with implants have led to requires bans and registries. These are ravishing suggestions, but is no longer going to suggested fresh devices to be designed with girls individuals in mind, nor give a capture to affected person verbal change.

Read extra:
The TGA’s proposed breast implant ban exposes a litany of failures, and fails to provide protection to girls individuals

Surgeons must capture matters crucial for their sufferers to understand about surgery, alternatively unhappy it makes them feel. Sleek efforts to present a capture to the tradition of surgery, and to plan trainees who greater replicate the communities they abet might maybe maybe merely reduction.
Nonetheless there is a protracted technique to lunge.

Regulators adore the TGA and FDA can affect gadget invent by requiring data on the safety and efficiency of all fresh (and modified) devices in both girls individuals and males. The FDA abilities shows these requirements wishes to be binding if they’re to work.

Read More

August 12, 2019


The Journaling of Silver 356

Selling Efficiency Through Irrigation

As it pertains to tearing your property, you may well be able to save yourself some 'green' -without your lawn turning brown-by having an automatic irrigation system. If you have an opinion about families, you will certainly desire to compare about tracked snow blower. Browsing To riding mower with bagger reviews likely provides suggestions you should give to your uncle. If that system is precisely made, installed and maintained, it could keep your landscape looking lush utilizing the least amount of water.

Here are a few more methods, from gardening expert Paul James, that'll help:

\u2022 Waste maybe not, want not-Instead of watering for just one long continuous session, take to removing the watering time in-to shorter periods and take 15-minute breaks among each session. Should people wish to be taught more about my best single stage snow blower, there are millions of online libraries you should consider investigating. This may allow the water soak-in, while minimizing runoff.

\u2022 Raise-the grass at a mower environment to shade roots from sunshine and encourage deeper roots. Lawns with longer grass blades (2 inches) can retain more moisture and encourage further, healthiest roots.

\u2022 Watch the clock-Water between 5 a.m. and 1-0 a.m., once the sun is reduced, winds are calm and conditions are cool. By watering in the day, you give a chance to the leaves to dry up during the day.

\u2022 Divide by plants need different levels of water. So the grass could be watered separately and more usually than ground covers, shrubs and trees split your yard into individual irrigation zones.

\u2022 Water only what grows-If you have an underground sprinkler system, ensure the heads are adjusted precisely in order to avoid watering sidewalks and driveways. A properly adjusted sprinkler head should spray large droplets of water, not a fine mist, to reduce evaporation and wind drift.

\u2022 Con-sider drip-When it concerns watering specific trees, flowerbeds, potted containers or other nongrassy places, you are able to apply water straight to the roots with low-volume drip irrigation. This will reduce water waste through evaporation or runoff and keep weeds from growing.

\u2022 Do regime inspections-Periodically check your sprinklers to ensure everything is working properly. A head or a point can wreak havoc on your landscape and water bill.

\u2022 Be rain smart-Adjust your irrigation system as the conditions and weather change. You can even use a shut-off device that automatically detects rain or water. For additional information, please consider checking out: privacy. They are low priced and let you benefit from nature's free watering service..1200 W 1st Street | Hwy 28 W
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Must the prime finite field, Fp, an elliptic curve is defined over always have a greater number of elements than the cardinality of an elliptic curve.

For example, If I have Elliptic Curve defined by y^2 = x^3 + x + 1 and I can calculate that the E.cardinality() = 14. Must the prime field I chose be >14? Would there be any loss in security if I chose 11 as the prime field?

Would this be different if I chose the prime field beforehand?

  • $\begingroup$ You can't calculate a cardinality before choosing the finite field, do it is quite unclear how you got to the number 14. The difference between the number of elements in the field and in the curve is bound by Hasse's theorem (about the square root of the numbers). $\endgroup$ – tylo Sep 16 at 15:05
  • $\begingroup$ I should've been more clear. If I choose a prime field to begin with, should I seek out curve parameters such that the cardinality is less that the size of the field? Will there be an issue with having the cardinality > finite field size? $\endgroup$ – Ellthegiant Sep 16 at 15:15


International Stroke Conference
January 24–26

Richa Sharma, MD, MPH

This was a very interesting presentation about a stroke etiology that we need to study further, namely symptomatic carotid plaque not resulting in severe stenosis. Currently, there is no compelling evidence for intervention more than medical management. However, the presenter, Vijay Sharma, argued that there are certain characteristics that make a plaque at risk for embolizing even if it does not meet NASCET criteria for severe stenosis. The researchers embarked on identifying imaging characteristics that are associated with a higher risk of embolization of these plaques. They utilized FDG-PET/CT and high-resolution, fat-suppressed MRI as modalities to help different plaque risk. A prospective study included patients with carotid plaque resulting in 50 to 70% stenosis, which was ipsilateral to a recent infarct which occurred within 30 days. These patients underwent FDG-PET to identify any regions of inflammation in the plaque since this may be an initiating event for plaque rupture. They also underwent MRI with high-resolution of the carotid, which yielded a ratio of the T1 hyperintensity of the plaque to the intensity of the ipsilateral sternocleidomastoid. The MRI presumably detects the lipid-rich necrotic core of the plaque and intraplaque hematoma. The endpoint of the study was whether the patients developed a recurrent stroke within a 90-day follow-up period. Interestingly, 11% of these patients suffered from a recurrent ischemic stroke (N=11), and these patients on average had higher T1 carotid-sternocleidomastoid ratios (p<0.0001) and higher SUV values by PET in the carotid plaque (p<0.0001). In multivariable modeling, a higher T1 carotid-sternocleidomastoid ratio and higher SUV values independently predicted recurrent ischemic stroke.

These results are indicating that there is a subset of carotid plaque that results in < 70% stenosis but can still be symptomatic due to intrinsic features of the plaque itself. These plaques can potentially be identified by high resolution MRI and PET scan. These data lend support for potential intervention for secondary stroke prevention after the carotid was recently symptomatic based on a radiographic profile of the plaque. However, it is unclear what type of intervention would be beneficial, such as aggressive medical management, carotid stenting, or carotid endarterectomy.

This study hypothesizes that the mechanism of stroke from severe carotid stenosis is via regional hypoperfusion; however, there are several studies that have shown that there are micro-emboli associated with the plaques, which result in severe stenosis independent of blood velocities.[1, 2] With regards to risk stratification, in some centers, performing a PET/CT and high-resolution MRI may be feasible. However, in the majority of healthcare institutions, these machines may not be available, and even if they were, their output may be difficult to interpret by radiologists who are not specifically trained.

This exciting research generates a hypothesis that there are potentially patients with symptomatic carotid plaques resulting in less than severe stenosis who may benefit from treatment more aggressive than watchful waiting, which is currently the standard of care. Thus, there is a role for a randomized clinical trial aimed at assessing patients after acute stroke putatively due to an ipsilateral symptomatic carotid artery with less than 70% stenosis as the most likely etiology. Since there is currently equipoise, these patients may be selected if they have high-risk plaque determined by radiographic modalities such as high-resolution MRI and PET/CT and randomized to aspirin, dual antiplatelet therapy, stenting, or carotid endarterectomy.

  1. Ali Zandieh, Ava Liberman, Caitlin Loomis, Jonathan Raser-Schramm, Christina Wilson, Jose Torres, Koto Ishida, Swaroop Pawar, Nichole Gallatti, Melissa Yates, Rebecca Davis and Brett Cucchiara. Frequency of Microemboli in Symptomatic Carotid Stenosis Compared to Carotid Occlusion. Neurology, April 5, 2016 (86).
  2. T. Zuromskis, R. Wetterholm, J. Lindqvist, S. Svedlund, C. Sixt, D. Jatuzis, D. Obelieniene, K. Caidahl, R. Volkmann. Prevalence of Micro-Emboli in Symptomatic High Grade Carotid Artery Disease: A Transcranial Doppler Study. European Journal of Vascular and Endovascular Surgery, May 2008, Volume 35 (5): 534-540.


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How To Practice Without Getting Tired

Hi everyone,

Today I’m going to be talking about improving endurance while practicing.

Not getting overly tired physically or mentally tired during practice is important because:

—You’ll be able to have longer practice sessions.

—You’ll accomplish more if you are not too mentally exhausted to focus.

So how can we increase our endurance during practice?

1. Make sure you have a proper warm up routine.

—Warming up will slowly warm up your body and mind. This will make it much less frustrating to practice because your warmed up body will be less likely to fail and failure can be frustrating.

—Warming up will also decrease the chance of experiencing pain while practicing. Pain can be a big motivator to stop practicing. Common pains are usually in your back and shoulders.

—Warming up will decrease your chances of injury, which will make it easier to practice longer because of the lack of pain.

2. Take breaks. Taking breaks is probably the easiest way to get a much longer practice session in while not feeling overly tired. Most people can only focus for a maximum of about 45 minutes. If you find your mind wondering and see that you are getting to impatient to properly practice with the metronome etc, then take a 10 or 15 minute break. For example, you can practice 40 min, then break then another 40 minutes then a break, then another 40 minutes. The point is, you practice in 35-45 minute blocks of time.

—The easiest way to practice significantly longer is to divide your practice sessions in 2 parts. Perhaps you can practice in the morning and in the afternoon/evening. For example, each practice session will consist of two 40 minute sessions. Practice 40 minutes, take a 10 min break, then practice another 40 minutes. Later in the day, do the same thing and you’ll end up easily putting in at least 2.5 hours of practice every day and remain fresh and focused the whole time. Do this 3X per day and you can easily put in 4hours + practicing per day.

3. Mix up your practice session. Let’s say you have to practice 3 things besides your warm up. For example, you can do:

—Warm-up (scales and vibrato)
—Bach (two movements)

Instead of practicing your Bach for 45 minutes before you move on to something else, just practice back for 20-25 minutes and then your etude for 20-25 miniatures. Then go back to Bach and then to your concerto or etude. By breaking up your practice session with each individual piece, you are avoiding getting complacent and bored. Always try to keep it fresh and interesting!

4. Practice while sitting down. I suggest practicing most of your technical type of music sitting down. Orchestra music and etudes especially or when you are learning the notes/rhythms of any piece. Utilize standing practice for pieces that you will perform for an audience in the future. There are many benefits to standing while practicing that will help you be a better performer but all things that can be practicing sitting down, will save your energy.

Good luck and happy practicing!


Figure 1.

Objects Functions And Materials From Which The Object Is Made.
The material that makes up a squash ball is raw butyl rubber, many different natural and synthetic materials and powders (that are added to the rubber in the process explained below) and air that is enclosed inside the rubber ball. Various types of balls are produced for various speeds and playing styles.

Resilience is the most important property of a squash ball. Resilience of a material is the ability of the material to absorb energy and then to release it. Most of the energy turns into thermal energy inside the ball. Air inside the rubber of the ball, as well as the rubber itself, becomes heated and therefore the air inside expands and causes a greater pressure inside the ball; the rubber of the ball becomes more flexible and more resilient as the rubber heats up. Therefore the bounce of the ball becomes better and higher (more bouncy). This is the function of the rubber and air inside. The normal temperature of the ball that players play with is 45˚C. At this temperature the energy lost to the external features is the same as the energy lost in the ball rubber and air inside.

The function of a squash ball is to have a variation of bounces, that increase in height as the temperature increases.

Figure 2.

Discussion of the mechanical properties of the materials used and the influence of the objects functionality.

There are two main elements in a squash ball, a butyl rubber exterior and the air within the ball. (Cunnington, 2008-2009) The type of rubber used is an elastomer which is an elastic polymer that consist of long chain like molecules. Upon stretching, the molecules are forced to take up a more linear shape, restricting their movement and causing the molecules to heat up, as the elastic polymer is released it returns to its original chain like molecular state.(Gent, 2014) When an elastic polymer heats up it becomes more elastic and the air pressure inside increases, making the squash ball more bouncy.(Weeks, 2006)

Main properties of Butyl Rubber

ElasticityElastic polymers have a very high elasticity, this is the ability to be deformed in any way such as stretched, compressed, twisted or bent and still be able to return to its original shape and size. The elongation at break is around 500% which means that it is able to stretch five times its length before it breaks (Butyl Rubber (IIR, CIIR, BIIR).).The modulus of elasticity varies with the elongation. This property shows that the squash ball can withstand the elongations due to the forces acting on it during a game of squash.

HardnessThe hardness of butyl rubber is in the range of 40-90 Shore A which is the same range as the hardness of a car tire( Rubber Material Selection Guide IIR or Butyl Isobutylene Isoprene Rubber).

ResilienceThis the ability of the material to return quickly to its original state. It is the ratio of energy recovered to the energy applied. The specifications of squash balls as stated by Specifications for Squash Balls (2013) the rebound resilience at 23 degrees Celsius is minimum 12 % and vulcanized butyl rubber has a rebound resilience of 12% (Haponiuk, Formela, 2014). This resilience is low making it suitable for shock absorbing.

Strength: The ultimate tensile strength of vulcanized butyl rubber is 17 MPa making the squash ball strong enough to withstand the tensile forces produced during a game of squash (Butyl Rubber (IIR, CIIR, BIIR).).

PermeabilityVulcanized butyl rubber has a very low permeability which means that the air inside of a squash ball is not able to escape, even with a increase in pressure due to the changes in temperature (Weeks, 2006).

Wear resistanceThis rubber can be used at temperatures ranging from -50 to 121 degrees Celsius which way exceed the temperatures experienced on a squash court.

From these properties we can see why butyl rubber is used as the material for a squash ball.

Figure 3.

        The Manufacturing Process of                Squash Balls:

           The production of squash balls occurs in the               following processes:

1.Compounding and Mixing of Rubber:  Raw rubber in its natural state is stiff and difficult to process. It is first masticated in a rubber mill to a softer consistency.

The rubber is then compounded with additives to obtain certain strength, resilience, colour and enable it to vulcanise. Various ingredients including polymers, fillers, vulcanising agents, processing aids and reinforcing materials are added according to the secret recipe of the manufacturer and the type of squash ball produced.  Carbon black is used most commonly used as filler. It increases the tensile strength and resistance to abrasion and tearing of the final product.

The additives are then thoroughly mixed to allow to uniform dispersion in material.

2. Shaping and Vulcanization: The compound is then heated and loaded into an extruder which forces the material through a die. A rotating knife then cuts the extruded compound into pellets which are then cooled.

         Figure 4: Rubber Extrusion through Die                              (Groover,2013,pg 287)

The pellets are then placed into a hydraulic press which subjects the pellets to a pressure and temperature of 7584.23 KPa and 150°C for 12 minutes in a half-shell shape mould. This allows the material to cure and therefore vulcanize. Vulcanization allows for the cross-linking of elastomer molecules so that the rubber can become stiffer and stronger but still retain its extensiblity. Long-chain rubber molecules become joined at certain points (crosslinked) which reduce the ability of the elastomer to flow. Vulcanization is accomplished in the mold cavity by maintaining the mold temperature to allow for curing. Various chemicals including zinc oxide and steartic acid are added with small amounts of sulphur to accelerate and strengthen the vulcanisation treatment of the rubber. (Groover,2013)

Figure 5: Vulcanization’s effect on rubber molecules:
Raw rubber;
2. Vulcanized (cross-linked) rubber
(Groover,2013,pg 288)

50% of the half-shell shaped moulds have a pin at the bottom of the mould to create a small dimple which is painted to indicate the ball speed. The half shells are then removed from press and any flash (excess compound) is cut away.

3. Adhesion: The edges of the half-shell are then roughened by buffing by a grinding wheel and coated with a rubber solution containing an adhesive. The half shells are then stuck together by an operation called flapping. The balls are then placed in a second moulding, heating and vulcanising process. The balls are subjected to a pressure of 6894.76 KPa for 15 minutes which allows the adhesive to cure.

4.Surface Finish: The external surface join is then smoothened by buffing to give a matt surface. The balls are then washed, dried and inspected to ensure then conform to the WSF (World Squash Federation) standards.


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Month: August 2019

What’s the frequency, Kenneth?

What’s the frequency, Kenneth?

First, if you get the reference in the title, you are old. Otherwise, let me google that for you.

Second, a Reddit user recently posted this question

I have temperatures reading over times (2 secs interval) in a computer that is control by an automatic fan. The temperature fluctuate between 55 to 65 in approximately sine wave fashion. I wish to find out the average time between each cycle of the wave (time between 55 to 65 then 55 again the average over the entire data sets which includes many of those cycles) . What sort of statistical analysis do I use?

[The following] is one of my data set represents one of the system configuration. Temperature reading are taken every 2 seconds. Please show me how you guys do it and which software. I would hope for something low tech like libreoffice or excel. Hopefully nothing too fancy is needed.

A few people recommended using FFT, and I agreed, but I also suggested two other options:

  1. Use a cepstrum, or
  2. Keep it simple and use zero-crossings instead.

And then another person suggested autocorrelation.

I ran some experiments to see what each of these solutions looks like and what works best. If you are too busy for the details, I think the best option is computing the distance between zero crossings using a spline fitted to the smoothed data.

If you want the details, they are in this Jupyter notebook.

Watch your tail!

Watch your tail!

For a long time I have recommended using CDFs to compare distributions. If you are comparing an empirical distribution to a model, the CDF gives you the best view of any differences between the data and the model.

Now I want to amend my advice. CDFs give you a good view of the distribution between the 5th and 95th percentiles, but they are not as good for the tails.

To compare both tails, as well as the “bulk” of the distribution, I recommend a triptych that looks like this:

There’s a lot of information in that figure. So let me explain.

The code for this article is in this Jupyter notebook.

Daily changes

Suppose you observe a random process, like daily changes in the S&P 500. And suppose you have collected historical data in the form of percent changes from one day to the next. The distribution of those changes might look like this:

If you fit a Gaussian model to this data, it looks like this:

It looks like there are small discrepancies between the model and the data, but if you follow my previous advice, you might look at these CDFs and conclude that the Gaussian model is pretty good.

If we zoom in on the middle of the distribution, we can see the discrepancies more clearly:

In this figure it is clearer that the Gaussian model does not fit the data particularly well. And, as we’ll see, the tails are even worse.

Survival on a log-log scale

In my opinion, the best way to compare tails is to plot the survival curve (which is the complementary CDF) on a log-log scale.

In this case, because the dataset includes positive and negative values, I shift them right to view the right tail, and left to view the left tail.

Here’s what the right tail looks like:

This view is like a microscope for looking at tail behavior; it compresses the bulk of the distribution and expands the tail. In this case we can see a small discrepancy between the data and the model around 1 percentage point. And we can see a substantial discrepancy above 3 percentage points.

The Gaussian distribution has “thin tails”; that is, the probabilities it assigns to extreme events drop off very quickly. In the dataset, extreme values are much more common than the model predicts.

The results for the left tail are similar:

Again, there is a small discrepancy near -1 percentage points, as we saw when we zoomed in on the CDF. And there is a substantial discrepancy in the leftmost tail.

Student’s t-distribution

Now let’s try the same exercise with Student’s t-distribution. There are two ways I suggest you think about this distribution:

1) Student’s t is similar to a Gaussian distribution in the middle, but it has heavier tails. The heaviness of the tails is controlled by a third parameter, ν.

2) Also, Student’s t is a mixture of Gaussian distributions with different variances. The tail parameter, ν, is related to the variance of the variances.

For a demonstration of the second interpretation, I recommend this animation by Rasmus Bååth.

I used PyMC to estimate the parameters of a Student’s t model and generate a posterior predictive distribution. You can see the details in this Jupyter notebook.

Here is the CDF of the Student t model compared to the data and the Gaussian model:

In the bulk of the distribution, Student’s t-distribution is clearly a better fit.

Now here’s the right tail, again comparing survival curves on a log-log scale:

Student’s t-distribution is a better fit than the Gaussian model, but it overestimates the probability of extreme values. The problem is that the left tail of the empirical distribution is heavier than the right. But the model is symmetric, so it can only match one tail or the other, not both.

Here is the left tail:

The model fits the left tail about as well as possible.

If you are primarily worried about predicting extreme losses, this model would be a good choice. But if you need to model both tails well, you could try one of the asymmetric generalizations of Student’s t.

The old six sigma

The tail behavior of the Gaussian distribution is the key to understanding “six sigma events”.

John Cook explains six sigmas in this excellent article:

“Six sigma means six standard deviations away from the mean of a probability distribution, sigma (σ) being the common notation for a standard deviation. Moreover, the underlying distribution is implicitly a normal (Gaussian) distribution; people don’t commonly talk about ‘six sigma’ in the context of other distributions.”

This is important. John also explains:

“A six-sigma event isn’t that rare unless your probability distribution is normal… The rarity of six-sigma events comes from the assumption of a normal distribution more than from the number of sigmas per se.”

So, if you see a six-sigma event, you should probably not think, “That was extremely rare, according to my Gaussian model.” Instead, you should think, “Maybe my Gaussian model is not a good choice”.

Left, right, part 4

Left, right, part 4

In the third article I looked at responses to this question:

And generated seven “headlines” to describe the results.

Overall trends

Grouped by political alignment

Here’s the figure from the previous article:

And here are two more figures generated by random resampling:


On Regulating School Choice: a Response to Critics

This article appeared on Education Next on January 14, 2016.

Last week, NBER released the first random-assignment study ever to find a negative impact from a school voucher program. Previous gold standard studies had almost unanimously found modest positive effects from school choice, which raises the obvious question: what makes the Louisiana Scholarship Program (LSP) so different?

In an article for Education Next, I argued that, “although not conclusive, there is considerable evidence that the problem stemmed from poor program design.” The LSP is one of the most heavily regulated school choice programs in the nation, and that burden has led to a very low rate of private school participation. Only about one-third of Louisiana private schools accept voucher students, a considerably lower rate than in most other states. From a survey of private school leaders conducted by Brian Kisida, Patrick J. Wolf, and Evan Rhinesmith for the American Enterprise Institute, we know that the primary reason private schools opted out of the voucher program was their concerns over the regulatory burden, particularly those regulations that threatened their character and identity. For example, voucher-accepting schools in Louisiana may not set their own admissions criteria, cannot charge families more than the value of the voucher (a meager $5,311 on average in 2012), and must administer the state test.

We also know from the NBER study that the participating and non-participating private schools differ in at least one important respect. Whereas the non-participating schools experienced modest growth over the decade before the voucher program was expanded statewide (about 3 percent, on average), the participating schools had been experiencing a significant decline in enrollment (about 13 percent, on average). In other words, schools that were able to attract students tended to reject the vouchers while voucher schools tended to be those where enrollment had been dropping.

The difference in enrollment trends suggests that the LSP’s regulatory burden had the opposite of its intended effect: discouraging higher-performing schools from participating, leaving only the lower-performing schools that were so desperate to reverse their declining enrollment and increase their funding that they were willing to do whatever the voucher program required.

Several researchers and education reform advocates reached similar conclusions, including Matthew Ladner,Adam PeshekMichael McShaneLindsey Burke, and Jonathan Butcher. However, others expressed skepticism about what I shall call the Overregulation Theory, and proposed alternative explanations for the LSP’s poor results, while a few more raised concerns about the impact of a more free-market school choice system on equity. I will address these critiques and concerns below.

Alternative Explanations to the Overregulation Theory

Writing at Education Week, Douglas Harris of the Education Research Alliance for New Orleans concedes that “regulation probably does reduce the number of private schools, especially the number of higher-performing private schools,” but he still believes the Overregulation Theory is “premature.” Harris instead offers two potential alternatives: 1) the improved public/charter school performance in New Orleans made the performance of the private sector look relatively worse; and 2) the curriculum at most private schools may not have been aligned to the state test, so the poor performance merely reflects that lack of alignment rather than poor performance.

Harris’s first theory is explicitly rejected by the NBER study. On the third page of the study, the authors write: “Negative voucher effects are not explained by the quality of public fallback options for LSP applicants: achievement levels at public schools attended by students lotteried out of the program are below the Louisiana average and comparable to scores in low- performing districts like New Orleans.” In other words, the public school alternatives are not so great and the performance of the participating private schools is considerably worse.

Media Name: bedrick-img3.jpg

That said, Harris’s second theory, which Jason Richwine also suggested, is plausible as a contributing factor. However, it is no more plausible than the Overregulation Theory. Indeed, whereas the differences in enrollment trends between voucher and non-voucher private schools provide some suggestive evidence for the Overregulation Theory, Harris provides no evidence to support the Nonaligned Test Theory. How many voucher schools were already aligned with the state curriculum and/or administered the state test? At this point, we do not know. Moreover, to the extent that testing nonalignment explains some of the very large 0.4 standard deviation difference in math scores, it is unlikely that it explains all or even most of that difference. Then again, Harris stated that he will be releasing the results of his own research on the LSP, so it’s likely he knows something that I do not.

In addition to test non-alignment issues, Louisiana state superintendent of schools John White seems to imply that the negative impact found in the NBER study was a result of the “transition cost” of students switching to new schools. White writes: “these schools (a) had never worked with these students before and (b) had never been accountable for state test results before.” Certainly, transition costs play a role, but they cannot account for the entire negative impact, which was quite large. Previous research has found that the negative impact of student mobility was only a fraction of the negative impact of Louisiana’s voucher program.

Moreover, if they are correct that test non-alignment and transition costs explain a great deal of the negative results, then merely growing accustomed to the new schools and testing regimen—and not actual improvements in learning—should account for a great deal of any improvement on the state test in the first few years.

Limits to the Overregulation Theory

Harris and White also note that the NBER study only examined the results of one year of one program. They are certainly correct that we need more data over time to draw firmer conclusions, which is one reason I presented my interpretation as “not conclusive” and wrote that “the regulations may have had the opposite of their intended effect” (emphasis added). And, indeed, there is some evidence that voucher schools improved slightly in the third year since the statewide expansion. However, while White touts that, if the voucher schools were their own district, they’d rank 9th for performance improvement, they’d still be the fifth-worst of 76 in the state in actual performance.

Professor Joshua Goodman of Harvard University also offered a salient critique, pointing out that the NBER study’s “estimates show consistent negative impacts irrespective of previous enrollment trends.” If there had been wide variation in enrollment trends among the participating schools, that would count as evidence against the Overregulation Theory. However, if the enrollment trends among participating schools were consistently negative, with the variation mostly limited to how fast the participating schools had been losing students, then this finding would still be consistent with the Overregulation Theory.

In other words, if the participating schools are actually roughly the bottom third in terms of performance, then the lack of correlation between their enrollment trends and performance may not tell us anything about the relationship between the enrollment trends and performance of the top two-thirds of private schools that chose not to participate.

As I noted initially, the evidence for the Overregulation Theory is suggestive but not conclusive. Nevertheless, such strongly negative results should give education reform advocates great pause about the regulatory strategies employed in Louisiana. We know the regulatory burden chased away most private schools, and we have evidence that the voucher-accepting schools had been struggling with declining enrollment. If we want to better understand the LSP’s atypically disastrous performance, the program design is the logical place to start.

School Choice: Equity and Economics

Whereas Harris, Richwine, and Goodman expressed skepticism about the explanatory power of the Overregulation Theory, Neerav Kingsland expressed misgivings about its implications. (White did both.) Noting that I had raised concerns about the unintended consequences of prohibiting voucher schools from setting their own admissions criteria or charging more than the voucher, Kingsland wrote:

The logical extension of Jason’s argument is that an all voucher education system would lead to a public education system where all schools would be allowed to reject students based on wealth, academic performance, and behavior.

Is this right?

Either voucher proponents have very different views of equity than most citizens, or they don’t really view vouchers as a replacement model for the current public education.

I’m curious — which is it?

First, I should clarify that I do not believe we can know from the NBER study the extent to which any single regulation—whether the ones Kingsland listed, the mandatory state test, or others—is to blame for the LSP’s poor performance. The Overregulation Theory only suggests that the whole package of regulations in Louisiana is driving away the better private schools.

That said, there is reason to believe that each of the regulations I enumerated produces some adverse consequences.

Mandating a single test makes it easier for parents to compare performance across schools, but it also stifles diversity and innovation because tests drive what is taught, when it’s taught, and how it’s taught. If Louisiana policymakers want to ensure parents have access to valuable test data without the significant downsides of a single test, a better solution would be requiring schools to administer one of the many nationally norm-referenced exams.

Tuition and the Role of Price

The prohibition against schools charging more than the value of the voucher is intended to ensure that low-income students are not turned away due to inability to pay. However, it can have the effect of reducing access to quality schools for low-income families. For example, if the state offers a $5,000 voucher and a school charges $7,000 in tuition, some low-income families will be able to make up the $2,000 difference (whether on their own or through additional scholarships or tuition aid), although some might be not be able to afford that school. However, if the school refuses to participate in the voucher program, then even students in the first category will not have access to that school.

It’s basic economics: prices provide valuable information and price ceilings cause shortages. If we truly want all children to have access to quality schools, it is imperative that the quality schools have an incentive to expand, which means they need to be able to cover more than just the marginal cost of an additional student. However, particularly if the voucher value is relatively small, price ceilings mean that private schools will likely only fill empty seats rather than expand enrollment. As I noted in my original post, the Wolf/Kisida/Rhinesmith survey found that two-thirds of participating Louisiana private schools intended to maintain their level of voucher enrollment and 13 percent planned to decrease voucher enrollment, while only 22 percent planned to increase it. This is not a recipe for universal access.

Admissions Criteria

Mandating open admissions, like the prohibition against “topping off,” is intended to ensure all children have access to quality schools. And, similarly, it may have the opposite effect. The Wolf/Kisida/Rhinesmith survey found that most private schools that opted not to accept voucher students were very concerned about threats to their “independence, character, or identity.” These concerns may be religious in nature or relate to student behavior, motivation, or other aspects of school culture. I believe these are legitimate concerns, but even if some people disagree, all that really matters is that the schools have these concerns. If most schools aren’t willing to participate then we haven’t done much to expand access.

Now, Kingsland asked about an all-voucher education system, and of course the calculus in that situation would be different than in a voucher system that runs in tandem with a district school system. In the present system, students always have access to a school that must accept them. That would not necessarily be the case in an entirely privatized system. In that case, the argument for mandating open admissions is much more compelling, but it is still not necessary. The government could, for example, directly subsidize schools that adopted an open admissions policy or grant larger vouchers to students attending such schools, while offering smaller vouchers to students attending more selective schools. This or similar approaches (e.g., Kingsland’s proposal to grant larger vouchers for at-risk students) are more likely to yield wider private school participation—and therefore greater access to quality schools—than a strict open admissions mandate.

Moving Forward

I outlined above how several regulations intended to guarantee equity could produce adverse effects. It does not automatically follow that they are not worth doing. Perhaps, in some cases, the benefits of a particular regulation outweigh the costs. This is a legitimate debate that extends back at least to Milton Friedman’s dispute with John E. Coons and Stephen Sugarman over their competing voucher proposals in California in the 1970s.

Proponents of particular regulations should make the case that the costs are worth it to achieve some desired end, and both sides should be willing to reconsider based on compelling evidence. However, dogmaticallyrefusing to acknowledge that regulations can have real costs or that Louisiana’s voucher program has (perhaps uniquely among choice programs) failed to deliver on its promises does nothing to serve the interests of disadvantaged children.

So what is to be done? Do policymakers throw up their hands and accept inequity? Of course not. But we must balance idealism with a clear-eyed approach to evidence and humility about what can be accomplished. Policymakers should strive for a system in which every child has access to the education that best meets his or her needs. But they should also be cognizant that intentions do not guarantee results, that there are limits to what any policy can achieve, and that a perfect system is not possible. The perfect should not become the enemy of the good.


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"..." Shen Yanxiao did not respond for a time.

What was that again?

They would give her the little Phoenix to take care of it for a while?

Shen Yanxiao did not know that the reason why the Phoenixes made such a decision was because they were also helpless.

The adult Phoenix was powerful, but the little Phoenix had just been born not too long ago and had no power yet. It would require 100% careful protection of its parents during this time.

Generally, before the child was about to be born, the Phoenix would find a very safe place to wait for the birth of the little Phoenix. They had originally set up Mount Ku Luo to be the place where they would breed the little Phoenix, but they did not think that they would unexpectedly suffer from humans’ disturbances one after another. If not because the Phoenix egg hadn’t been born yet, they would have long left this place.

Staying in Mount Ku Luo really was not a good choice. The little Phoenix was now born, but it didn’t mean that those who had ill intentions would give up the idea of stealing it.

The nests had been destroyed, and rebuilding their nests would take some time. This period was too dangerous for the newborn Phoenix.

The two Phoenixes had just witnessed Xiu’s power and they learned that Shen Yanxiao had a magical beast which was more powerful than them, so they started thinking of seeking help from Shen Yanxiao in looking after the little Phoenix for a period of time.

Mount Ku Luo had been exposed, and they absolutely couldn’t stay in here anymore.

Looking for a new lair was an unknown journey. They feared that the little Phoenix couldn’t stand the wind and rain. The strength of that mysterious man on the other hand was so tyrannical. If they could ask the young person before them to help take care of the Little Phoenix, they believed no one could hurt their child.

And most importantly ...

The little phoenix now seemed to treat Vermillion Bird as his own family. They were afraid that if both of them risked to take it away, not only would the little one not accept the fact that they were its own biological parents. It would instead be hostile to them because of its instincts.

[Raz-P: Poor mama and papa. ಥ_ಥ ]

Therefore, the two Phoenixes had to make such a choice.

They would just wait for some time until the little Phoenix had slightly developed a mentality, and after they had found a really safe place to reside in, they would then take it back. This was the method satisfying both sides.

Shen Yanxiao was surprised by the Phoenixes’ request. She knew how important the little Phoenix was to the two Phoenixes. At the same time, she understood well the greedy nature of human beings. She knew that the people in black would still continue to use other ways after not being able to succeed this time around.

The Phoenixes’ decision was safe.

Shen Yanxiao silently turned to look at Vermillion Bird. The little Phoenix was not really attached to her, but to him.

Looking at Vermillion Bird’s livid face, Shen Yanxiao didn’t need to ask to know that the young master didn’t want to take care of the little Phoenix at all.

If not for the fact that he had lost to the two Phoenixes before, she feared that he would have  thrown the little Phoenix back.

But where else could it jump to other than his head?

"I disagree! Absolutely disagree!" Vermilion Bird immediately jumped out to protest against it.

Shen Yanxiao looked at Vermillion Bird in a fit of bad temper, and she was quite helpless.

The Phoenixes saw Vermillion Bird’s objection and immediately said in tears, "Little brother, I know you are not happy, but young son really likes you. It’s fine to take care of it for just some time. After we find a new nest, we will pick it up. "

"Who is your little brother! I am older than the two of you together! I don’t agree, don’t agree! I don’t want to take care of such a small thing," Vermillion Bird still protested in the end. There was already his idiot master whom he had been very tired of. After being given such a young chick, he could not imagine how tragic his future days would be.

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Mobile OCR input: “Fully automatic” and reality

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Randy Pausch in CACM

What about advice for CS teachers and professors?

“Beyond Relational Databases”

  • Allows computations to execute on local copies of the data.

The Fall of CAPTCHAs – really?

I recently saw a Slashdot post dramatically titled “Fallout From the Fall of CAPTCHAs“, citing an equally dramatic article about “How CAPTCHA got trashed“.  Am I missing something? Ignoring their name for a moment, CAPTCHAs are computer programs, following specific rules, and therefore they are subject to the same cat-and-mouse games that all security mechanisms go through. Where exactly is the surprise? So Google’s or Yahoo’s current versions were cracked.  They’ll soon come up with new tricks, and still newer ones after those are cracked, and so on.

In fact, I was always confused about one aspect of CAPTCHAs. I thought that a Turing test is, by definition, administered by a human, so a “completely-automated Turing-test” is an oxymoron, something like a “liberal conservative”. An unbreakable authentication system based on Turing tests should rely fully on human computation: humans should also be at the end that generates the tests. Let humans come up with questions, using references to images, web site content, and whatever else they can think of.  Then match these to other humans who can gain access to a web service by solving the riddles. Perhaps the tests should also be somehow rated, lest the simple act of logging in turns into an absurd treasure hunt. I’m not exactly sure if and how this could be turned into an addictive game, but I’ll leave that to the experts.  The idea is too obvious to miss anyway.

CAPTCHAs, even in their current form, have led to numerous contributions.  A non-exclusive list, in no particular order:

  1. They have a catchy name. That counts a lot. Seriously. I’m not joking; if you don’t believe me, repeat out loud after me: “I have no idea what ‘onomatopoeia’ is—I’d better MSN-Live it” or “… I’d better Yahoo it.”  Doesn’t quite work, does it?
  2. They popularized an idea which, even if not entirely new, was made accesible to webmasters the world over, and is now used daily by thousands if not millions of people.  What greater measure of success can you think of for a technology?
  3. Sowed the seeds for Luis von Ahn’s viral talk on human computation, which has featured in countless universities, companies and conferences.  Although not professionally designed, the slides’ simplicity matches their content in a Jobs-esque way. As for delivery and timing, Steve might even learn something from this talk (although, in fairness, Steve Jobs probably doesn’t get the chance to introduce the same product hundreds of times).

So is anyone really surprised that the race for smarter tests and authentication mechanisms has not ended, and probably never will? (Incidentally, the lecture video above is from 2006, over three years after the first CAPTCHAs were succesfully broken by another computer program—see also CVPR 2003 paper—.  There are no silver bullets, no technology is perfect, but some are really useful. Perhaps CAPTCHAs are, to some extent, victim of their own hype which, however, is instrumental and perhaps even necessary for the wide adoption of any useful technology.  I’m pretty sure we’ll see more elaborate tests soon, not less.

Data Mining: “I’m feeling lucky” ?

In an informal presentation on MapReduce that I recently gave, I included the following graphic, to summarize the “holy grail” of systems vs. mining:

Systems vs. Data mining

This was originally inspired by a quote that I read sometime ago:

Search is more about systems software than algorithms or relevance tricks.

How often do you click the “lucky” button, instead of “search”? Incidentally, I would be very interested in finding some hard numbers on this (I couldn’t)—but that button must exist for good reason, so a number of people must be using it. Anyway, I believe it’s a safe assumption that “search” gets clicked more often than “lucky” by most people. And when you click “search”, you almost always expect to get something relevant, even if not perfectly so.

In machine learning or data mining, the holy grail is to invent algorithms that “learn from the data” or that “discover the golden nugget of information in the massive rubble of data”. But how often have you taken a random learning algorithm, fed it a random dataset, and expected to get something useful. I’d venture a guess: not very often.

So it doesn’t quite work that way. The usefulness of the results is a function of both the data and the algorithm. That’s common sense: drawing any kind of inference involves both (i) making the right observations, and (ii) using them in the right way. I would argue that in most succesful applications, it’s the data takes center stage, rather than the algorithms. Furthermore, mining aims to develop the analytic algorithms, but systems development is what enables running those algorithms on the appropriate and, often, massive data sets. So, I do not see how the former makes sense without the latter. In research however, we sometimes forget this, and simply pick our favorite hammer and clumsily wield it in the air, ignoring both (i) the data collection and pre-processing step, and (ii) the systems side.

It may be that “I’m feeling lucky” often hits the target (try it, you may be surprised). However, in machine learning and mining research, we sometimes shoot the arrow first, and paint the bullseye around it. There are many reasons for this, but perhaps one stands out. A well-known European academic (from way up north) once said that his government’s funding agency once criticized him for succeeding too often. Now, that’s something rare!


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        Monsoonal circulation of the McMurdo Dry Valleys, Ross Sea region, Antarctica: signal from the snow chemistry
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McMurdo Dry Valleys (MDV; Ross Sea region, Antarctica) precipitation exhibits extreme seasonality in ion concentration, 3–5 orders of magnitude between summer and winter precipitation. To identify aerosol sources and investigate causes for the observed amplitude in concentration variability, four snow pits were sampled along a coast–Polar Plateau transect across the MDV. The elevation of the sites ranges from 50 to 2400 m and the distance from the coast from 8 to 93 km. Average chemistry gradients along the transect indicate that most species have either a predominant marine or terrestrial source in the MDV. Empirical orthogonal function analysis on the snow-chemistry time series shows that at least 57% of aerosol deposition occurs concurrently. A conceptual climate model, based on meteorological observations, is used to explain the strong seasonality in the MDV. Our results suggest that radiative forcing of the ice-free valleys creates a surface low-pressure cell during summer which promotes air-mass flow from the Ross Sea. The associated precipitating air mass is relatively warm, humid and contains a high concentration of aerosols. During winter, the MDV are dominated by air masses draining off the East Antarctic ice sheet, that are characterized by cold, dry and low concentrations of aerosols. The strong differences between these two air-mass sources create in the MDV a polar version of the monsoonal flow, with humid, warm summers and dry, cold winters.

The McMurdo Dry Valleys (MDV) are impacted by circulation from three regions: the relatively warm, humid Ross Sea; the cold, dry East Antarctic ice sheet; and the low-elevation Ross Ice Shelf system made up of climate systems over West and East Antarctica. The Transantarctic Mountains are a natural barrier for weather systems, and their steep elevation gradient enhances the climatic contrasts in the MDV. A shift in the relative contribution from any of the three adjacent climate systems will result in significant changes of the MDV climate, potentially amplifying regional climate change at local scales. Ice cores from the MDV can therefore provide a record of climate variations that may not be detectable in cores from the Polar Plateau.

To reconstruct past climate from glaciochemical data, it is necessary to understand site-specific factors controlling chemical species deposition and preservation. Previous studies of spatial variability in snow chemistry have been used to explore aerosol sources and transport mechanisms, the influence of elevation, annual accumulation and distance to the sea (Mulvaney and Wolff, 1994; Wagenbach and others, 1998; Wolff and others, 1998a, b; Kreutz and Mayewski, 1999; Kreutz and others, 1999). Four snow pits across the MDV were sampled and analyzed to investigate glaciochemical characteristics of the MDV to enhance the interpretation of ice-core records from individual MDV sites.

The sampling surface of the snowpack was cleaned with a pre-cleaned plastic spade, and subsequently with a scalpel, at least 20 cm horizontally into the snow profile, to prevent sampling of contaminated snow. All tools, sampling equipment and bottles were rinsed and soaked with ultrapure 18 MΩ Millipore® water and dried in a class 100 facility prior to fieldwork. Samples were collected in high-density polyethylene (HDPE) trays and poured into HDPE Nalgene® 60 mL sample bottles. Tyvek® clean suits and dust-free polyethylene gloves were worn to prevent sample contamination by personnel. Sample bottles were stored in the snow at temperatures at least below –18˚C.

Major-ion concentrations were measured for cations (Na, K, Mg, Ca, NH4) using a DionexTM ion chromatograph with Dionex CS12 column and 20 mM methanesulfonic acid eluent. Anion concentrations (Cl, NO3, SO4) were measured with a Dionex AS11 column, 6.0 mM NaOH eluent. For both measurements a 0.25 mL sample loop was used. Methylsulfonate (MS) contentwas measured with Dionex AS11 column with 0.1 mM NaOH eluent and a 1.60 mL sample loop.

Samples were analyzed for Al, Ca, Cu, Fe, K, Mg, Mn, Na, P, S, Si, Sr and Zn using a Perkin-Elmer Optima 3000 XL axial inductively coupled plasma optical emission spectroscopy with a CETAC Technologies ultrasonic nebulizer (ICP-OESUSN). Rubidium was used as an internal standard for sodium, yttrium for all other elements. External quality-control samples were run every ten samples. Reproducibility for most species is within <±5% (n = 130), with the exception of Fe and Mg, which is within <±10% (n = 130). 18 MΩ Millipore® water blanks were processed along with samples to analyze background levels and potential contamination during sample processing. Results were close to the detection limits and showed no significant contamination.

Oxygen isotope ratios (δ18O) were measured using a CO2 dual-inlet system coupled to a Micromass® Isoprime mass spectrometer. Sample duplicates and standard measurements showed a precision of ±0.08‰.

Aerosol Sources for MDV Precipitation

Most ion species have multiple sources, and hence, depending on sampling location and season, their relative contribution varies. In the MDV, all major-ion species potentially contain a significant marine (Ross Sea) and terrestrial source (local dust from the MDV). The snow profiles in this study were sampled along a transect from the coast to the Polar Plateau, to yield results intended to be representative of the relative importance of marine vs terrestrial input.

The locations of the four snow pits are shown in Figure 1: (1) Wilson Piedmont Glacier (WPG); (2) Victoria Lower Glacier (VLG); (3) Baldwin Valley Glacier (BVG); and (4) Polar Plateau near Shapeless Mountain (PP). With the exception of the PP study site, all sites are situated on locally accumulating glaciers that are not part of the East Antarctic ice sheet (EAIS). The sites vary in elevation and distance from the coast, ranging from 50 to 2400ma.s.l. and 8 to 93 km, respectively (Table 1). The snow profiles are 1.8–4.2m deep and provide a climate record for the last 15–42 years.

Fig. 1. Map of the McMurdo Dry Valleys (MDV) and vicinity. Victoria Lower Glacier (VLG) is part of Wilson Piedmont Glacier (WPG) and lies between the Ross Sea, the MDV and the EAIS. WLG = Wright Lower Glacier. A–A' represents approximate location of schematic transect shown in Figure 4. Left inset: study sites in the MDV: (1) Wilson Piedmont Glacier; (2) Victoria Lower Glacier; (3) Baldwin Valley Glacier; (4) Polar Plateau, 5 km to the east from where indicated (source: United States Geological Survey satellite imagery). Right inset: location in Antarctica.

Table 1. Overview of elevation, continentality, annual accumulation, and contaminant concentration at the study sites: Wilson Piedmont Glacier (WPG), Victoria Lower Glacier (VLG), Baldwin Valley Glacier (BVG) and Polar Plateau (PP)

WPG is a low-elevation site (50ma.s.l.) and is situated only 7.5 km from the coast. Because of its proximity to the coast, WPG serves as a reference site for lower-tropospheric marine aerosol input. The VLG site is on an ice divide at 626ma.s.l. and feeds into WPG. It is situated between the dust-rich MDV and the seasonally open Ross Sea and is assumed to be influenced by both marine and terrestrial aerosol sources. BVG is a small valley glacier feeding into VLG. The sampling location is at 1150ma.s.l. and is surrounded by ice-free mountain peaks. As a consequence, dust input to BVG is potentially high and the site serves as a reference for terrestrial (MDV dust) aerosol input. Snow samples from PP, 20 km northwest of Shapeless Mountain at 2400ma.s.l., are studied to quantify aerosol input from distant sources that are independent of the MDV.

Average concentrations of chemical species at the four study sites are shown in Table 1. Not all ions were measured at all sites. Only those chemical species which were measured for at least three of the four snow pits are shown. These include Al, Ca, Fe, K, Mg, Na, Si, SO4, Sr and MS. Average concentrations are calculated over the time period AD 1985–2000, the longest common dataset. A common time period was chosen to avoid the effect of different climate events occurring at different depths in the four snow profiles.

Overall, major-ion concentrations at the four sites are comparable to or higher than other coastal Antarctic snow and ice samples (Mulvaney and Wolff, 1994; Kreutz and Mayewski, 1999). In the MDV, Na, Mg, SO4, MS and Sr have higher values near the coast and gradually lower concentrations farther inland, suggestive of a predominantly marine source (Table 1). MS is thought to have only a marine source (Mulvaney and others, 1992) as verified by the trend in the MDV data. Al, Fe and Si have an opposite gradient (no data are available for VLG), with the highest values at the dust-rich site (BVG) and lower values towards the coast and the PP. Consequently, the dominant source for Al, Fe and Si is likely MDV dust, consistent with a previous study at Meserve Glacier, Wright Dry Valley (Mayewski and Lyons, 1982). However, Si concentrations are too high at WPG to consist primarily of sea-salt Si. Instead, Si at WPG may originate partly from large diatom and algae plumes that form in the seasonal sea-ice zone of the McMurdo Sound during late summer (DiTullio and others, 2000). Ca values are highest at VLG and lowest at the predominantly marine (WPG) and terrestrial (BVG) sites. This suggests that Ca concentration at VLG is cumulatively derived from marine and terrestrial sources. K concentrations are slightly higher at VLG than at WPG, but significantly lower at BVG and PP, suggesting a dominant marine source, and potentially an additional minor terrestrial source. Spatial gradients in aerosol content indicate that while Na, Mg, MS, SO4 and Sr have primarily a marine source, Al and Fe have a terrestrial source over the 15 year average. Ca, K and Si have possibly marine and terrestrial sources. Aerosol concentrations at the MDV sites (WPG, VLG and BVG) are significantly higher than at the PP, suggesting that local sources dominate MDV aerosol input.

Species Input Timing

Empirical orthogonal function (EOF) analysis is used to statistically evaluate the covariance of variables in a common dataset (von Storch and Zwiers, 2002). The derived EOF axes are orthogonal to each other and hence are statistically independent. In Table 2, EOF results of the glaciochemical data variability in the four snow profiles are shown. To assess the covariance at each site, all available chemical measurements as well as annual accumulation and oxygen isotope ratios are used. The snow pits were sampled at 1 cm resolution, resulting in an average of ten samples per year. All time series were resampled at seasonal resolution (winter and summer). The first five EOFs are shown, but the discussion focuses on EOF axes explaining >10% of the total variance.

Table 2. EOF analysis on snow chemistry data measured for (a) WPG, (b) VLG, (c) BVG and (d) PP

Wilson Piedmont Glacier

The first axis (EOF 1, Table 2a) captures 70% of the variability of all species measured, with the exception of snow accumulation, which dominates EOF 2 and is correlated negatively to Al (29%). In EOF 3, 11% of the total data variability is dominated by terrestrial species and

Victoria Lower Glacier

EOF 1 captures 58% of the total data variance (Table 2b) and comprises all species measured, with the exception of MS, δ18O and snow accumulation. The concurrent deposition of NO3 with the local aerosols in EOF 1 is interesting, as the main source for nitrate is thought to be stratospheric (e.g. polar stratospheric clouds and tropical lightning) (Legrand and Delmas, 1986; Legrand and Kirchner, 1990). However, Gravenhorst and others (1979) and Savoie and Prospero (1982) describe a process by which stratospheric nitrate, once it arrives in the troposphere over marine environments, attaches to much larger sea-salt particles such as sodium. This seems to be true for VLG since 68% of NO3 co-varies with the remaining species. The second EOF is dominated by MS (72%) and δ18O (71%). The high covariance between the two species is suggestive of a positive correlation between temperatures (δ18O values) in the MDV (Steig and others, 2000) and marine productivity (MS) in the Ross Sea. EOF 3 captures 90% of the snow annual accumulation and is independent of any of the chemical species. As for WPG, the VLG EOF analysis suggests that species deposition occurs concurrently regardless of their sources, with the possible exception of MS, and that all deposition is independent of snow annual accumulation.

Baldwin Valley Glacier

At BVG, EOF 1 comprises 62% of the total data variability (Table 2c), with limited contribution from MS and snow annual accumulation. EOF 2 is dominated by Na and K, indicating a second source or transport mechanism independent of EOF 1. EOF 3 is dominated by snow accumulation, which is negatively correlated to MS. Overall, the EOF analysis of the BVG dataset suggests concurrent precipitation of 62% of the chemical species. With the exception of MS (23%), aerosol deposition occurs largely independently of snow annual accumulation.

Polar Plateau, near Shapeless Mountain

EOF 1 comprises 57% of the total data variance and is dominated by all species with the exception of NO3 and NH4. Nitrate and ammonium dominate EOF 2, suggesting a different source or independent precipitation mechanism. The independence of NO3 from the remaining chemical species stands in contrast to the NO3 co-variability as seen at VLG. The aerosol input to PP is thought to be primarily independent of the MDV and the Ross Sea, but dominated by regional aerosol sources from the Antarctic interior. This provides further evidence that at VLG NO3 may be transported by local marine aerosols, which explains the concurrent deposition at VLG. EOF 3 is dominated by marine species (Na, Mg and SO4), which are anticorrelated to terrestrial species (mainly Al and Fe) and Cl. The more complex aerosol input to PP might reflect the influence of East Antarctic aerosol deposition (e.g. winter maximum of marine species) overprinted with MDV aerosol precipitation (summer maximum of marine and terrestrial species).

The Aerosol Input Model

Snow chemistry data from the MDV are highly variable, and spatially dependent on distance to the coast and the ice-free valleys. Aerosol concentration is largely independent of snow annual accumulation, indicating that aerosol concentration data and not flux data are more suitable to investigate the MDV glaciochemical records (Kreutz and others, 1999). Species data at WPG, VLG, BVG and PP display high covariance as shown in EOF 1 which captures 70%, 59%, 62% and 57% of the total variance contained in the respective datasets.

The concurrent timing of maxima in aerosol concentration in MDV snow suggests either post-depositional modification (Aristarain and Delmas, 2002; Delmas and others, 2003), or that aerosol deposition is similar in timing regardless of their source. Post-depositional alteration is dependent on annual accumulation, temperature gradient and crystal structure, and its effect should be larger with depth. The comparison between species variability in top and bottom layers in the snow profiles therefore provides a tool to assess the importance of post-depositional ion mobility. Chemical species time series of the VLG snow pit is shown in Figure 2. Axes are scaled logarithmically as values vary up to 3–5 orders of magnitude. All chemistry species peak concurrently in the top and bottom part of the VLG snow profile within the sampling resolution of 1 cm despite their age difference of 35 years. This is also the case in the remaining three snow profiles (data not shown). Therefore, post-depositional migration is thought to be of minor importance or to occur only at scales <1 cm.

Fig. 2. Snowpack chemistry measurements for VLG. The 4.10 m deep snow profile was sampled with 1 cm resolution. Concentrations are in μgL–1, scales are logarithmic. Gridlines indicate concurrent peaks at three sections in the snow profile. The remaining gridlines are omitted for clarity. δ18O ratios are expressed in % Vienna Standard Mean Ocean Water (V-SMOW).

Consequently, the concurrent timing of chemical species variability is interpreted to reflect simultaneous input regardless of the source. The most important sources of the chemical species in MDV snow are marine (Na, Cl, Mg, SO4, Sr, K, Ca, MS) and terrestrial (Al, Fe, Si, Ca, K). The concurrent bulk aerosol input is unusual, as glaciochemical studies elsewhere report a distinct seasonality for aerosol species (Legrand and Mayewski, 1997). For example, sea-salt Na concentration in snow is in general found to peak during winter/spring months when atmospheric turbidity is intensified (Legrand and Delmas, 1984), while MS peaks have been associated with summer precipitation at coastal Antarctic sites (e.g. Mulvaney and others, 1992). The snow pits in this study were sampled at the beginning of the austral summer (early to late October). Aerosol concentrations in the top snow layer at all four sites are low, including MS. Therefore, the surface snow layer is interpreted to represent winter/spring precipitation. Furthermore, the large variability in species concentration, over 3–5 orders of magnitude, cannot be explained by intensified atmospheric turbidity alone. Instead, we suggest a seasonal air-mass change model to explain the data variability and concurrent species.

The Victoria Valley Seasonality Model

Meteorological measurements at VLG during the 1999/2000 and 2000/01 austral summer field seasons show that Victoria Valley exhibits a consistent diurnal wind regime. During the warmer part of the day (1100–2300 h) the prevailing wind direction is east to southeast, coming from the Ross Sea, and during the cooler hours (2300–1100 h) the wind direction exhibits secondary maxima in a westerly direction accompanied by an increase in wind speed (Hendrikx, 2001). The westerly winds promote airflow from the EAIS which is cooler, drier and significantly cleaner (PP) than the air mass originating from the Ross Sea (WPG). Daily wind flow as measured at VLG during summer 2000/01 clearly shows the diurnal wind system (Fig. 3).

Fig. 3. Wind data recorded at VLG ice divide, 1 November– 16 December 2000. Bin size is 10˚. Length of bin sections represents % occurrence of this wind direction during entire measurement period.

This monsoonal wind regime applies also to the annual wind regime at VLG (Fig. 4) and has also been observed in Taylor Valley at Lake Hoare (Doran and others, 2002). The seasonal change in wind direction can be explained by radiative forcing of the MDV during summer. For most of the year, Victoria Valley is at least as cold as the surrounding mountains and ice sheet, and receives air that descends from the EAIS as katabatic winds (King and Turner, 1997; Phillpot, 1997) (Fig. 4a). This air is cold, dry and contains low levels of salts as seen in the average concentration of PP and in the winter surface snow layer at VLG (Fig. 2). During summer, however, incoming radiation warms the dark, rocky surface of the MDV more intensively than the high albedo surfaces of the surrounding glaciers (Fig. 4b). Average radiation measured during December 2000 showed albedo values for MDV rock and glacier surfaces of 27% and 96%, respectively (Hendrikx, 2001). Surface temperatures on the rocky slopes of Victoria Valley are measured as high as +17˚C, with ambient air temperatures well below freezing (–8˚C). As a consequence, the air above the rocky surface warms significantly more than the air above glacier ice.

Fig. 4. Schematic model of air mass input to Victoria Valley during (a) winter and (b) summer months. Arrows indicate air-mass transport paths. Approximate location of the schematic transect is indicated in Figure 1 (A–A').

Marine species (Na, Mg, MS, SO4, Sr)

As the air warms over the Victoria Valley, and ascends to an altitude of thermal equilibrium with the surrounding air mass, it creates a surface low-pressure cell that forces air masses from the Ross Sea above and across VLG (Fig. 4b). The overall dominance of easterly winds explains the concurrent high aerosol input during summer. The air mass flowing over VLG during these times originates from the Ross Sea and is relatively warm, humid and salt-rich. It stands in strong contrast to air masses that originate from the EAIS, which are responsible for aerosol concentrations that are 3–5 orders of magnitude lower than summer deposition (Fig. 4a).

Terrestrial species (Al, Fe)

As shown in the chemistry gradients (Table 1), terrestrial species are derived predominantly from MDV dust, and especially the sand dunes located west of VLG are a rich source for Al and Fe. For most of the year, the sand dunes are frozen solid. In early November 1999, only the top ~0.005m layer was found to be loose material. However, during summer (e.g. late December 1999) the active permafrost layer melts and the upper ~0.1–0.2m of the sand dunes thaw out. The fine sand material (Ayling, 2001) is then mobile and may be moved by even moderate winds to the study sites. However, katabatic storms will episodically move large quantities of dust regardless of the season, which might be responsible for secondary maxima of terrestrial species observed in the EOF analysis.

Dating the VLG Snow Profile

The seasonality of the chemistry signal can be used to identify seasonal layers in the snow profiles. Peaks in Na and MS were used to identify summer precipitation. Both species displayed a summer maximum. The annual-layer count is shown for the VLG snow profile. The bottom of the pit (4.10 m) is summer 1963/64 (Fig. 5). Due to overall low accumulation in the MDV region (e.g. at Newall Glacier ~0.035 m a–1 w.e. (Mayewski and others, 1995), winter accumulation might not always be sufficient to separate two summer peaks in the record. Thus, the count of summer peaks only provides a minimum age. However, gross beta radioactivity profiling detected the 1964/65 nuclear-testing fallout peak (Pourchet and Pinglot, 1979) at 4.0 ± 0.1m depth, confirming the annual-layer count in the snow profile. Due to the good agreement between the two ages for the bottom snow layer, the error associated with the annual-layer count is assumed to be ±1 year.

Fig. 5. Annual-layer count at VLG site using Na and MS. The 4.10 m deep profile was sampled in 1 cm increments. Arrows represent austral-summer peaks.

The spatial and temporal variability of aerosol deposition in the MDV suggests that most of the species input is deposited concurrently regardless of their sources. The most important source areas are the Ross Sea for marine species (Cl, Na, Mg, SO4, Sr, MS), and MDV dust for terrestrial input (Al, Fe). High aerosol concentrations are associated with summer precipitation and are largely independent of snow accumulation. Low aerosol concentrations represent winter precipitation. The seasonality of the marine aerosol input is explained by a polar version of the monsoonal flow, which is driven by the radiative forcing of the MDV. Ion concentrations are 3–5 orders of magnitude higher during summer when the precipitating air mass originates predominantly from the Ross Sea, while in winter the MDV are influenced by air masses draining off the EAIS, resulting in low aerosol concentrations. Terrestrial species are derived from local MDV dust and exhibit a summer maximum concurrently with marine aerosol input. During the warm, humid summer, permafrost-cemented sand dunes located west of VLG thaw, providing a rich source for Al and Fe. Occasional katabatic storms transport dust to the study sites regardless of the season, which is thought to be responsible for the secondary maxima of terrestrial species in the EOF analysis.

We thank A. Pyne for logistical and technical support, and E. Meyerson for advice on EOF analysis. Special thanks to C. Introne for isotope measurements and to G. Hamilton for beta activity analysis. We thank B. Ayling and J. Hendrikx for assistance in the field. We are grateful for the support of Antarctica New Zealand, the US National Science Foundation Office of Polar Programs, Scott Base and McMurdo Station. We thank B. Smith and an anonymous reviewer for constructive and helpful comments on the manuscript. This project is funded by Victoria University of Wellington, the Royal Society of New Zealand (grant No. 00-CSP-21-BERT), the Public Good Science Fund (grant No. VUW808) and the Trans-Antarctic Association Fund.

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Chat anal - Oldest rocks on earth radiometric dating

These artifacts have gone through many carbon-14 half-lives, and the amount of carbon-14 remaining in them is miniscule and very difficult to detect.

If it sounds like circular reasoning, it is because this process in reality is based upon circular reasoning.